Collaborative Learning Agents

Papers from the AAAI Spring Symposium

Recent advances in the multiagent systems (MAS) field have generated optimism that widely applicable solutions to large, distributed problems may be at hand. However, before the field can deliver on that promise, the challenge of how to control such systems to address a pre-specified goal (e.g., minimize throughput of packets in data routing, win the game in soccer) in a decentralized, adaptive manner with minimal detailed hand-tuning needs to be met.

In this symposium we focused on two crucial properties that would allow a MAS to meet those challenges: (1) the agents need to work collectively so that as a group, their behavior solves the overall problem; and (2) both the agents and their collaborative structure need to be adaptive.

The first property is crucial in large problems (e.g., internet routing), and inherently distributed problems (e.g., planetary exploration rovers, constellations of satellites), in that it enables a modular approach to the problem. The importance of the second property lies in how the agents interact with one another and the environment. Because both the environment and the response of other agents to changes in that environment will modify the "background" state one agent perceives before choosing its actions, it is imperative that adaptivity be built in to those agents.

Our focus in this symposium was to address the design of systems that are intended to solve large distributed computational problems with little to no handtailoring through the collective and adaptive behavior of the agents comprising that system.